Workflows
What is a Workflow?Filters
Galaxy Workflow Documentation: MS Finder Pipeline
This document outlines a MSFinder Galaxy workflow designed for peak annotation. The workflow consists of several steps aimed at preprocessing MS data, filtering, enhancing, and running MSFinder.
Step 1: Data Collection and Preprocessing
Collect if the inchi and smiles are missing from the dataset, and subsequently filter out the spectra which are missing inchi and smiles.
1.1 MSMetaEnhancer: Collect InChi, Isomeric_smiles, and Nominal_mass
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Type: Galaxy
Creators: Zargham Ahmad, Helge Hecht, Elliott J. Price, Research Infrastructure RECETOX RI (No LM2018121) financed by the Ministry of Education, Youth and Sports, and Operational Programme Research, Development and Innovation - project CETOCOEN EXCELLENCE (No CZ.02.1.01/0.0/0.0/17_043/0009632).
Submitters: Helge Hecht, Zargham Ahmad
Importing single-end multiplexed data (not demultiplexed yet)
Type: Galaxy
Creators: Debjyoti Ghosh, Helmholtz-Zentrum für Umweltforschung - UFZ
Submitter: WorkflowHub Bot
Use DADA2 for sequence quality control. DADA2 is a pipeline for detecting and correcting (where possible) Illumina amplicon sequence data. As implemented in the q2-dada2 plugin, this quality control process will additionally filter any phiX reads (commonly present in marker gene Illumina sequence data) that are identified in the sequencing data, and will filter chimeric sequences.
Type: Galaxy
Creators: Debjyoti Ghosh, Helmholtz-Zentrum für Umweltforschung - UFZ
Submitter: WorkflowHub Bot
From the R1 and R2 fastq files of a single samples, make a scRNAseq counts matrix, and perform basic QC with scanpy. Then, do further processing by making a UMAP and clustering. Produces a processed AnnData Depreciated: use individual workflows insead for multiple samples
Takes fastqs and reference data, to produce a single cell counts matrix into and save in annData format - adding a column called sample with the sample name.
Take a scRNAseq counts matrix from a single sample, and perform basic QC with scanpy. Then, do further processing by making a UMAP and clustering. Produces a processed AnnData object.
Depreciated: use individual workflows insead for multiple samples
From the R1 and R2 fastq files of a single samples, make a scRNAseq counts matrix, and perform basic QC with scanpy. Then, do further processing by making a UMAP and clustering. Produces a processed AnnData
Depreciated: use individual workflows insead for multiple samples
Basic processing of a QC-filtered Anndata Object. UMAP, clustering e.t.c
Take an anndata file, and perform basic QC with scanpy. Produces a filtered AnnData object.
Takes fastqs and reference data, to produce a single cell counts matrix into and save in annData format - adding a column called sample with the sample name.
Tests